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This guide walks you through installing, licensing, and connecting the CData Python Connector to live BigQuery data. You will learn to:
Let's begin.
CData simplifies access and integration of live Google BigQuery data. Our customers leverage CData connectivity to:
Most CData customers are using Google BigQuery as their data warehouse and so use CData solutions to migrate business data from separate sources into BigQuery for comprehensive analytics. Other customers use our connectivity to analyze and report on their Google BigQuery data, with many customers using both solutions.
For more details on how CData enhances your Google BigQuery experience, check out our blog post: https://www.cdata.com/blog/what-is-bigquery
Python Dependencies Note: Make sure you have Python installed. The CData Python Connector supports Python versions 3.8, 3.9, 3.10, 3.11, and 3.12. If you are using a version outside this range, you may need to create a virtual environment with virtualenv.
pip install cdata_googlebigquery_connector-24.0.9111-cp312-cp312-win_amd64.whl
pip install cdata_googlebigquery_connector-24.0.####-python3.tar.gz
After your purchase, you should have received your license key via email from the CData Orders Team. The license key is a 25-character code that looks like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX
.\license-installer.exe [YOUR LICENSE KEY HERE]
./install-license.sh [YOUR LICENSE KEY HERE]
Can I use my license on multiple machines?
Yes, depending on your subscription tier. Check your order confirmation email or contact your account representative for details.
If you are unsure who your account representative is, contact [email protected].
I lost my license key. How do I retrieve it?
Email [email protected] with your order number, and we will resend your license key.
Can I transfer my license to a different machine?
Yes. You will need to submit a License Transfer Request using our license transfer request page linked below:
https://www.cdata.com/lic/transfer/
After your License Transfer Request is submitted and processed, an additional activation will be added to your Product Key.
You will then be able to activate the full license on the new machine.
Once this process is complete, the license on the previous machine will become invalid.
For additional licensing questions, contact [email protected]. You can view and manage your license through our self-service portal at portal.cdata.com.
After the installation and license activation are complete, you can establish a connection using the CData Python Connector.
The CData Python Connector for BigQuery is exposed as a Python module that you can import using the standard import statement and then build your application code around it.
The Connector also includes built-in metadata tools such as sys_tables and sys_tablecolumns, which allow you to perform schema discovery — including available tables, columns, and structural metadata for BigQuery data.
The following example establishes a connection to BigQuery using your authentication properties and retrieves column names from a specific table.
Replace or modify the connection string values below with your actual credentials, and update your table name in '[TABLE NAME]' as needed.
If your BigQuery instance uses MFA or additional security requirements, you may need to include properties such as Passcode or SecurityToken in your connection string. Refer to the Connection String Options section in the Connector Help documentation (also available inside the help directory of the Connector) for a complete list of supported properties.
import cdata.googlebigquery as mod
# Establish the connection using your configured properties
conn = mod.connect(
"DataSetId=MyDataSetId;"
"ProjectId=MyProjectId;"
"InitiateOAuth=GETANDREFRESH;"
)
# Query column names for the specified table
cur = conn.cursor()
cur.execute("SELECT ColumnName FROM sys_tablecolumns WHERE TableName = '[TABLE NAME]'")
print("Columns in your table:")
for row in cur.fetchall():
print(row[0])
cur.close()
conn.close()
This code connects to BigQuery, queries the metadata catalog, and prints all column names for the table you specify. Check out the complete Connector documentation to learn how to modify the SQL query to explore additional schemas, tables, or other supported metadata views.
Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.
OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.
In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.
Solution: Verify that your User, Password, and any additional authentication properties required by BigQuery are correct. If your data source enforces MFA, SSO, or passcodes, ensure the correct properties are included in the connection string. Refer to the complete Connector documentation for the full list of supported authentication properties, or contact [email protected] for assistance validating authentication settings.
Solution: Confirm that the endpoint URL in your connection string is correct and that outbound HTTPS traffic is allowed from your environment. If you are behind a firewall or proxy, ensure that Python is permitted to reach the service URL. For network configuration details or port requirements, contact [email protected].
Solution: Verify the Database, Schema, and table name in your SQL query. Use metadata views such as sys_tables and sys_tablecolumns to confirm the exact table and column names exposed by BigQuery data. If the table name is case-sensitive, ensure you are using the correct casing in your query.
Solution: Ensure the Python Connector is installed in the correct environment. Run pip list to verify that the connector (cdata-googlebigquery-connector) is present. If you are using virtual environments, activate the correct environment before executing your script.
Solution: Incorrect property formatting or missing semicolons can prevent the connector from parsing your connection settings. Review your connection string to ensure each property follows the correct Key=Value; format. Refer to the Python Connector documentation for property names supported by BigQuery.
For additional connection troubleshooting, contact [email protected] with your full error message (masking sensitive credentials before sending).
With the connector installed and your connection configured, you can now begin working with live BigQuery data in Python. Explore the resources below to extend your integration and build complex workflows.
| Python Client | Article Title |
|---|---|
| Python MCP Server | Connect BigQuery to AI Assistants With the CData Python MCP Server |
| pandas | Use pandas to Visualize BigQuery in Python |
| Dash | Use Dash & Python to Build Web Apps on BigQuery |
| SQLAlchemy | Use SQLAlchemy ORMs to Access BigQuery in Python |
| petl | Extract, Transform, and Load BigQuery in Python |
If you need assistance at any point:
SELECT * FROM sys_tables;
If your question is not covered in this FAQ, contact [email protected].
Download a Community License of the Google BigQuery Connector to get started:
Download NowLearn more:
👁 Google BigQuery IconPython Connector Libraries for Google BigQuery Data Connectivity. Integrate Google BigQuery with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
